Open Access
August 2005 How to Lie with Bad Data
Richard D. De Veaux, David J. Hand
Statist. Sci. 20(3): 231-238 (August 2005). DOI: 10.1214/088342305000000269


As Huff’s landmark book made clear, lying with statistics can be accomplished in many ways. Distorting graphics, manipulating data or using biased samples are just a few of the tried and true methods. Failing to use the correct statistical procedure or failing to check the conditions for when the selected method is appropriate can distort results as well, whether the motives of the analyst are honorable or not. Even when the statistical procedure and motives are correct, bad data can produce results that have no validity at all. This article provides some examples of how bad data can arise, what kinds of bad data exist, how to detect and measure bad data, and how to improve the quality of data that have already been collected.


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Richard D. De Veaux. David J. Hand. "How to Lie with Bad Data." Statist. Sci. 20 (3) 231 - 238, August 2005.


Published: August 2005
First available in Project Euclid: 24 August 2005

zbMATH: 1100.62533
MathSciNet: MR2189000
Digital Object Identifier: 10.1214/088342305000000269

Keywords: Accuracy , data consistency , data mining , data profiling , Data quality , data rectification , data warehousing , distortion , missing values , record linkage

Rights: Copyright © 2005 Institute of Mathematical Statistics

Vol.20 • No. 3 • August 2005
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